Big Data, Algorithmic Governmentality and the Regulation of Pandemic Risk
Big Data, Algorithmic Governmentality and the Regulation of Pandemic Risk
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DOI：
10.1017/err.2019.6
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发表时间：
2019-03-01
影响因子：
2.9
通讯作者：
Roberts, Stephen L.
中科院分区：
文献类型：
--
作者：
Roberts, Stephen L.
本文研究了算法疾病监测系统的兴起，作为在大数据时代用于监管大流行疫情的风险分析新技术。重要的是，本文展示了如何加强努力，利用大数据和应用算法处理技术，以加强实时监测和监管传染病爆发显着改变全球传染病监测的做法;通过新的风险理性的出现来观察，这些理性支撑了强化算法实践的部署，以越来越多地殖民和巡逻数据的紧急地形以识别和管理异常致病风险的出现。从概念上讲，本文进一步断言了这些新的风险监管技术在大数据背景下的兴起如何改变了政府和流行病和大流行病的预测：在大数据，疾病监测和大流行病监管的当代背景下，新兴的算法政府风险的兴起。
This article investigates the rise of algorithmic disease surveillance systems as novel technologies of risk analysis utilised to regulate pandemic outbreaks in an era of big data. Critically, the article demonstrates how intensified efforts towards harnessing big data and the application of algorithmic processing techniques to enhance the real-time surveillance and regulation infectious disease outbreaks significantly transform practices of global infectious disease surveillance; observed through the advent of novel risk rationalities which underpin the deployment of intensifying algorithmic practices to increasingly colonise and patrol emergent topographies of data in order to identify and govern the emergence of exceptional pathogenic risks. Conceptually, this article asserts further howthe rise of these novel risk regulating technologies within a context of big data transforms the government and forecasting of epidemics and pandemics: illustrated by the rise of emergent algorithmic governmentalties of risk within contemporary contexts of big data, disease surveillance and the regulation of pandemic.